Do you know how to leverage next-gen technologies like automation and AI to deliver core value to your business? Check out our new thought leadership: Validation of Machine Learning Models.
The potential of machine learning (ML) to deliver value to banks has created something of a gold rush in adopting this methodology for banking applications. ML can produce immense benefits when applied to complex nonlinear problems where there is a large amount of data, particularly unstructured data. Use cases for incorporating machine learning in banking include asset management, fraud detection, credit risk management and regulatory compliance, to name a few. More specifically, large banks are turning to ML models as an alternative to traditional models to gain faster, more accurate and more insightful predictions and classifications in their risk management and financial management business decisions.
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By: Ken Thomas & Kathie Topel
By: Matt Kotraba, Eli Hajjar & Ryan Clapman
Like many new technologies, robotic process automation (RPA) is disrupting traditional solutions and introducing different risks related to its use. Bots act differently from other traditional software and services because they leverage the same interfaces as a human user, but they are inherently not human. A bot’s access needs to be managed, and there are several factors to consider. Bots bring about new risks and challenges around identity and access management (IAM) and cannot be treated like a human identity.
By: Andrew Allen
Organizations always ask us the same three things: Can we really prevent a breach? Can we detect a breach? Are we currently breached? Staying ahead of each of these three scenarios requires an organization to be proactive and think about effectively detecting threats.Thinking purple involves bringing the organization’s offensive (blue) and defensive (red) cyber security teams together to determine how well their cyber tooling investment and business procedures perform.
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